JSEQ_scRNAseq - REPORT
2025-11-20
The report was generated using the JSEQ_scRNAseq single-cell analysis pipeline. For more information, please visit: https://github.com/jkubis96/JSEQ_scRNAseq
1 Analysis Configuration Parameters
| Parameter | Value |
|---|---|
| mt_per | 25 |
| scale_factor | 1e+06 |
| n_features | 2000 |
| c_res | 0.5 |
| heterogeneity | deg |
| mt_cssg | FALSE |
| m_val | 0.05 |
| top_m | 50 |
| max_genes | 1000 |
| max_combine | 1000 |
| loss_val | 0.05 |
| s_factor | 0.8 |
| p_bin | 0.05 |
| drop | TRUE |
| min_c | 10 |
The parameters above were used during the current analysis.
If you need to apply different analysis conditions, modify the parameters in the configuration file.
Parameters description:
- mt_per
Maximum percentage of mitochondrial genes per cell.
Default: 25% - down
Lower threshold for the number of genes per cell.
Default: NA (automatically computed if NA) - up
Upper threshold for the number of genes per cell.
Default: NA (automatically computed if NA) - scale_factor
Scale factor used for data normalization. - n_features
Number of variable features to detect for clustering. - c_res
Clustering resolution — higher values produce more clusters. - heterogeneity
Method for estimating cluster heterogeneity.
Options:var(within-cluster variance),deg(deregulated gene profiles).
Default: deg - mt_cssg
Whether to include mitochondrial genes when creating subclasses and subtypes.
Options:TRUE,FALSE.
Default: FALSE - m_val
Maximum p-value threshold for marker detection in advanced subtype analyses.
Default: 0.05 - top_m
Maximum number of top markers used for naming clusters based on effect size metrics.
Default: 50 - max_genes
Maximum number of input genes considered in cluster heterogeneity discovery.
Default: 1000 - max_combine
Maximum number of initial combinations for each iteration during heterogeneity discovery.
Default: 1000 - loss_val
Assigned value for potentially unclassified cells within a cluster. - s_factor
Maximum split factor for gene occurrence in heterogeneity discovery (0.2–1).
Default: 0.8 - p_bin
Minimum cell proportion required for population presence based on binomial test p-value.
Default: 0.05 - min_c
Minimum cell proportion required for population presence as defined by the user (independent of binomial test).
Default: 10 - drop
Boolean indicating whether to drop non-significant subtypes based onp_binandmin_c.
Default: TRUE
The configuration file is available in the directory:
JSEQ_scRNAseq/requirements_file/config_file.conf
Additional configuration files for other analysis steps can also be found in:
JSEQ_scRNAseq/requirements_file
For more information, please visit:
https://github.com/jkubis96/JSEQ_scRNAseq
2 Cell content analysis
2.1 Ratio of number of genes to counts
2.2 Number of genes and counts per cell
2.3 Percentage of ribosomal and mitochondrial genes [%]
3 Quality control of cell content
3.1 Genes per cell content & thresholds
3.2 Genes upper & lower thresholds per cell
3.3 Number of cells across different stages of analysis
4 Gene expression analysis across cells
4.1 Top highly variable genes in the dataset
5 Princilpe component selection
5.1 ElbowPlot - principal components cutoff
5.2 JackStrawPlot - PC significance
